Cargando…

Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method

Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for charac...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Mengxuan, Tian, Shanshan, Sun, Linlin, Chen, Xi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479843/
https://www.ncbi.nlm.nih.gov/pubmed/30978981
http://dx.doi.org/10.3390/s19071737
_version_ 1783413438625611776
author Li, Mengxuan
Tian, Shanshan
Sun, Linlin
Chen, Xi
author_facet Li, Mengxuan
Tian, Shanshan
Sun, Linlin
Chen, Xi
author_sort Li, Mengxuan
collection PubMed
description Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann–Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice.
format Online
Article
Text
id pubmed-6479843
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-64798432019-04-29 Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method Li, Mengxuan Tian, Shanshan Sun, Linlin Chen, Xi Sensors (Basel) Article Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann–Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice. MDPI 2019-04-11 /pmc/articles/PMC6479843/ /pubmed/30978981 http://dx.doi.org/10.3390/s19071737 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Mengxuan
Tian, Shanshan
Sun, Linlin
Chen, Xi
Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
title Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
title_full Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
title_fullStr Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
title_full_unstemmed Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
title_short Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
title_sort gait analysis for post-stroke hemiparetic patient by multi-features fusion method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479843/
https://www.ncbi.nlm.nih.gov/pubmed/30978981
http://dx.doi.org/10.3390/s19071737
work_keys_str_mv AT limengxuan gaitanalysisforpoststrokehemipareticpatientbymultifeaturesfusionmethod
AT tianshanshan gaitanalysisforpoststrokehemipareticpatientbymultifeaturesfusionmethod
AT sunlinlin gaitanalysisforpoststrokehemipareticpatientbymultifeaturesfusionmethod
AT chenxi gaitanalysisforpoststrokehemipareticpatientbymultifeaturesfusionmethod